Human Ag-tracking analysis
Ryan Sheridan
2024-07-03
The fraction of predicted Ag-competent cells is shown below for each sample.
The total number of predicted Ag-competent cells is shown below for each sample.
The expression of Ag-low and Ag-high gene modules is shown for predicted Ag-low and Ag-competent cells.
UMAP projections show predicted Ag-low and Ag-competent cLECs for each sample.
Expression of LEC markers is shown below for each sample.
R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=en_US.UTF-8
[9] LC_ADDRESS=en_US.UTF-8 LC_TELEPHONE=en_US.UTF-8
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=en_US.UTF-8
time zone: America/Denver
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ranger_0.15.1 clustifyr_1.12.0 clustifyrdata_1.1.0
[4] gprofiler2_0.2.2 ggtext_0.1.2 patchwork_1.1.3
[7] scales_1.2.1 M3Drop_1.26.0 numDeriv_2016.8-1.1
[10] openxlsx_4.2.5.2 xlsx_0.6.5 MetBrewer_0.2.0
[13] presto_1.0.0 data.table_1.14.8 Rcpp_1.0.11
[16] cowplot_1.1.1 knitr_1.44 colorblindr_0.1.0
[19] colorspace_2.1-0 qs_0.25.5 ggforce_0.4.1
[22] ggtrace_0.2.0.9000 djvdj_0.1.0 lubridate_1.9.3
[25] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.3
[28] purrr_1.0.2 readr_2.1.4 tidyr_1.3.0
[31] tibble_3.2.1 ggplot2_3.4.4 tidyverse_2.0.0
[34] here_1.0.1 SeuratObject_4.1.4 Seurat_4.4.0
[37] biomaRt_2.56.1
loaded via a namespace (and not attached):
[1] matrixStats_1.0.0 spatstat.sparse_3.0-2
[3] bitops_1.0-7 httr_1.4.7
[5] RColorBrewer_1.1-3 tools_4.3.1
[7] sctransform_0.4.0 backports_1.4.1
[9] utf8_1.2.3 R6_2.5.1
[11] lazyeval_0.2.2 uwot_0.1.16
[13] mgcv_1.8-42 withr_2.5.1
[15] sp_2.1-0 prettyunits_1.2.0
[17] gridExtra_2.3 progressr_0.14.0
[19] cli_3.6.1 Biobase_2.60.0
[21] spatstat.explore_3.2-3 labeling_0.4.3
[23] entropy_1.3.1 sass_0.4.7
[25] mvtnorm_1.2-3 spatstat.data_3.0-1
[27] ggridges_0.5.4 pbapply_1.7-2
[29] QuickJSR_1.0.7 StanHeaders_2.26.28
[31] foreign_0.8-84 parallelly_1.36.0
[33] bbmle_1.0.25 rstudioapi_0.15.0
[35] RSQLite_2.3.1 generics_0.1.3
[37] RApiSerialize_0.1.2 gtools_3.9.4
[39] ica_1.0-3 spatstat.random_3.1-6
[41] zip_2.3.0 inline_0.3.19
[43] loo_2.6.0 Matrix_1.6-1.1
[45] fansi_1.0.5 S4Vectors_0.38.2
[47] abind_1.4-5 lifecycle_1.0.3
[49] yaml_2.3.7 SummarizedExperiment_1.30.2
[51] gplots_3.1.3 BiocFileCache_2.8.0
[53] Rtsne_0.16 grid_4.3.1
[55] blob_1.2.4 promises_1.2.1
[57] crayon_1.5.2 bdsmatrix_1.3-6
[59] reldist_1.7-2 miniUI_0.1.1.1
[61] densEstBayes_1.0-2.2 lattice_0.21-8
[63] xlsxjars_0.6.1 KEGGREST_1.40.1
[65] pillar_1.9.0 fgsea_1.26.0
[67] GenomicRanges_1.52.1 future.apply_1.11.0
[69] codetools_0.2-19 fastmatch_1.1-4
[71] leiden_0.4.3 glue_1.6.2
[73] vctrs_0.6.3 png_0.1-8
[75] gtable_0.3.4 cachem_1.0.8
[77] xfun_0.40 S4Arrays_1.0.6
[79] mime_0.12 survival_3.5-5
[81] SingleCellExperiment_1.22.0 rJava_1.0-6
[83] statmod_1.5.0 ellipsis_0.3.2
[85] fitdistrplus_1.1-11 ROCR_1.0-11
[87] nlme_3.1-162 bit64_4.0.5
[89] progress_1.2.2 filelock_1.0.2
[91] RcppAnnoy_0.0.21 rstan_2.32.3
[93] GenomeInfoDb_1.36.4 rprojroot_2.0.3
[95] bslib_0.5.1 irlba_2.3.5.1
[97] KernSmooth_2.23-21 rpart_4.1.19
[99] BiocGenerics_0.46.0 DBI_1.1.3
[101] Hmisc_5.1-1 nnet_7.3-19
[103] processx_3.8.2 tidyselect_1.2.0
[105] bit_4.0.5 compiler_4.3.1
[107] curl_5.1.0 htmlTable_2.4.2
[109] xml2_1.3.5 DelayedArray_0.26.7
[111] plotly_4.10.2 stringfish_0.15.8
[113] checkmate_2.3.0 caTools_1.18.2
[115] lmtest_0.9-40 callr_3.7.3
[117] rappdirs_0.3.3 digest_0.6.33
[119] goftest_1.2-3 spatstat.utils_3.0-3
[121] rmarkdown_2.25 XVector_0.40.0
[123] htmltools_0.5.6.1 pkgconfig_2.0.3
[125] base64enc_0.1-3 MatrixGenerics_1.12.3
[127] dbplyr_2.3.4 fastmap_1.1.1
[129] rlang_1.1.1 htmlwidgets_1.6.2
[131] shiny_1.7.5 farver_2.1.1
[133] jquerylib_0.1.4 zoo_1.8-12
[135] jsonlite_1.8.7 BiocParallel_1.34.2
[137] RCurl_1.98-1.12 magrittr_2.0.3
[139] Formula_1.2-5 GenomeInfoDbData_1.2.10
[141] munsell_0.5.0 reticulate_1.32.0
[143] stringi_1.7.12 zlibbioc_1.46.0
[145] MASS_7.3-60 plyr_1.8.9
[147] pkgbuild_1.4.2 parallel_4.3.1
[149] listenv_0.9.0 ggrepel_0.9.3
[151] deldir_1.0-9 Biostrings_2.68.1
[153] splines_4.3.1 gridtext_0.1.5
[155] tensor_1.5 hms_1.1.3
[157] ps_1.7.5 igraph_1.5.1
[159] spatstat.geom_3.2-5 reshape2_1.4.4
[161] stats4_4.3.1 rstantools_2.3.1.1
[163] XML_3.99-0.14 evaluate_0.22
[165] RcppParallel_5.1.7 tzdb_0.4.0
[167] tweenr_2.0.2 httpuv_1.6.11
[169] RANN_2.6.1 polyclip_1.10-6
[171] future_1.33.0 scattermore_1.2
[173] xtable_1.8-4 later_1.3.1
[175] viridisLite_0.4.2 memoise_2.0.1
[177] AnnotationDbi_1.62.2 IRanges_2.34.1
[179] cluster_2.1.4 timechange_0.2.0
[181] globals_0.16.2